On-line Evolving Image Classifiers and Their Application to Surface Inspection

In this paper, we present image classifiers which are able to adapt and evolve themselves at an on-line machine vision system. These classifiers are initially trained on some pre-labelled training data and further updated based on newly recorded samples, for instance during a production process. The evolution and adaptation mechanism is necessary in order to guarantee a process-save on-line system as usually the pre-labelled data does not cover all possible operating conditions, system states or image classes. It is also recommended for a refinement of the classifiers during the on-line mode in order to boost predictive performance with more loaded samples. We will present two types of on-line evolving image classifiers: The first one is a clustering-based classification approach, which exploits conventional vector quantization, forming an incremental evolving variant around it and extending it to the supervised classification case. The second one is an evolving fuzzy classifier approach which comes with two model architectures, classical single model and a novel multi-model architecture, the later exploiting indicator matrices/vectors for training. The approaches are evaluated in three different on-line surface inspection systems dealing with CD imprint inspection, egg inspection and inspection of metal rotor parts. The evaluation will show the impact of on-line evolved versus 'static' classifiers kept fixed during the whole on-line process.